{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gensim word vector visualization of various word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Get the interactive Tools for Matplotlib\n",
    "# %matplotlib notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import gensim.downloader as api\n",
    "from gensim.models import KeyedVectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For looking at word vectors, I'll use Gensim. We also use it in hw1 for word vectors. Gensim isn't really a deep learning package. It's a package for word and text similarity modeling, which started with (LDA-style) topic models and grew into SVD and neural word representations. But its efficient and scalable, and quite widely used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll use our homegrown Stanford offering of GloVe word vectors. Gensim provides a library of several sets of word vectors that you can easily load. You can find out more about GloVe on [the Glove page](https://nlp.stanford.edu/projects/glove/). I use the 100d vectors below as a balance between speed and smallness vs. quality. If you try out the 50d vectors, they basically work for similarity but clearly aren't as good for analogy problems. If you load the 300d vectors, you'll wait longer, but they're even better than the 100d vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'gensim.models.keyedvectors.KeyedVectors'>\n"
     ]
    }
   ],
   "source": [
    "model = api.load(\"glove-wiki-gigaword-100\")\n",
    "print(type(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.66146  ,  0.94335  , -0.72214  ,  0.17403  , -0.42524  ,\n",
       "        0.36303  ,  1.0135   , -0.14802  ,  0.25817  , -0.20326  ,\n",
       "       -0.64338  ,  0.16632  ,  0.61518  ,  1.397    , -0.094506 ,\n",
       "        0.0041843, -0.18976  , -0.55421  , -0.39371  , -0.22501  ,\n",
       "       -0.34643  ,  0.32076  ,  0.34395  , -0.7034   ,  0.23932  ,\n",
       "        0.69951  , -0.16461  , -0.31819  , -0.34034  , -0.44906  ,\n",
       "       -0.069667 ,  0.35348  ,  0.17498  , -0.95057  , -0.2209   ,\n",
       "        1.0647   ,  0.23231  ,  0.32569  ,  0.47662  , -1.1206   ,\n",
       "        0.28168  , -0.75172  , -0.54654  , -0.66337  ,  0.34804  ,\n",
       "       -0.69058  , -0.77092  , -0.40167  , -0.069351 , -0.049238 ,\n",
       "       -0.39351  ,  0.16735  , -0.14512  ,  1.0083   , -1.0608   ,\n",
       "       -0.87314  , -0.29339  ,  0.68278  ,  0.61634  , -0.088844 ,\n",
       "        0.88094  ,  0.099809 , -0.27161  , -0.58026  ,  0.50364  ,\n",
       "       -0.93814  ,  0.67576  , -0.43124  , -0.10517  , -1.2404   ,\n",
       "       -0.74353  ,  0.28637  ,  0.29012  ,  0.89377  ,  0.67406  ,\n",
       "        0.86422  , -0.30693  , -0.14718  ,  0.078353 ,  0.74013  ,\n",
       "        0.32658  , -0.052579 , -1.1665   ,  0.87079  , -0.69402  ,\n",
       "       -0.75977  , -0.37164  , -0.11887  ,  0.18551  ,  0.041883 ,\n",
       "        0.59352  ,  0.30519  , -0.54819  , -0.29424  , -1.4912   ,\n",
       "       -1.6548   ,  0.98982  ,  0.27325  ,  1.009    ,  0.94544  ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model['bread']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.25144  ,  0.52157  , -0.75452  ,  0.28039  , -0.31388  ,\n",
       "        0.274    ,  1.1971   , -0.10519  ,  0.82544  , -0.33398  ,\n",
       "       -0.21417  ,  0.22216  ,  0.14982  ,  0.47384  ,  0.41984  ,\n",
       "        0.69397  , -0.25999  , -0.44414  ,  0.58296  , -0.30851  ,\n",
       "       -0.076455 ,  0.33468  ,  0.28055  , -0.99012  ,  0.30349  ,\n",
       "        0.39128  ,  0.031526 , -0.095395 , -0.004745 , -0.81347  ,\n",
       "        0.27869  , -0.1812   ,  0.14632  , -0.42186  ,  0.13857  ,\n",
       "        1.139    ,  0.14925  , -0.051459 ,  0.37875  , -0.2613   ,\n",
       "        0.011081 , -0.28881  , -0.38662  , -0.3135   , -0.1954   ,\n",
       "        0.19248  , -0.52995  , -0.40674  , -0.25159  ,  0.06272  ,\n",
       "       -0.32724  ,  0.28374  , -0.2155   , -0.061832 , -0.50134  ,\n",
       "        0.0093959,  0.30715  ,  0.3873   , -0.74554  , -0.45947  ,\n",
       "        0.40032  , -0.1378   , -0.26968  , -0.3946   , -0.64876  ,\n",
       "       -0.47149  , -0.085536 ,  0.092795 , -0.034018 , -0.61906  ,\n",
       "        0.19123  ,  0.20563  ,  0.29056  , -0.010908 ,  0.15313  ,\n",
       "        0.33144  ,  0.33806  ,  0.061708 ,  0.20785  ,  0.65348  ,\n",
       "       -0.053222 ,  0.18589  ,  0.32647  , -0.11923  ,  0.42008  ,\n",
       "       -0.26931  ,  0.025489 ,  0.0036535,  0.1327   , -0.22763  ,\n",
       "        0.07564  ,  0.55773  ,  0.2978   ,  0.28144  ,  0.19775  ,\n",
       "       -0.23582  ,  0.65303  ,  0.089897 ,  0.35844  ,  0.14304  ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model['croissant']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('canada', 0.6544384956359863),\n",
       " ('america', 0.645224392414093),\n",
       " ('u.s.a.', 0.6184033155441284),\n",
       " ('united', 0.6017189621925354),\n",
       " ('states', 0.5970699191093445),\n",
       " ('australia', 0.583871603012085),\n",
       " ('world', 0.5590084791183472),\n",
       " ('2010', 0.558070182800293),\n",
       " ('2012', 0.5504006743431091),\n",
       " ('davis', 0.5464467406272888)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('usa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('coconut', 0.7097253203392029),\n",
       " ('mango', 0.705482542514801),\n",
       " ('bananas', 0.6887733936309814),\n",
       " ('potato', 0.6629636287689209),\n",
       " ('pineapple', 0.6534532308578491),\n",
       " ('fruit', 0.6519854068756104),\n",
       " ('peanut', 0.6420577168464661),\n",
       " ('pecan', 0.6349172592163086),\n",
       " ('cashew', 0.6294420957565308),\n",
       " ('papaya', 0.6246590614318848)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('banana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('croissants', 0.6829845309257507),\n",
       " ('brioche', 0.6283302903175354),\n",
       " ('baguette', 0.5968103408813477),\n",
       " ('focaccia', 0.5876684784889221),\n",
       " ('pudding', 0.5803957581520081),\n",
       " ('souffle', 0.5614769458770752),\n",
       " ('baguettes', 0.5558239817619324),\n",
       " ('tortilla', 0.5449503064155579),\n",
       " ('pastries', 0.5427730679512024),\n",
       " ('calzone', 0.5374532341957092)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar('croissant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('shunichi', 0.49618101119995117),\n",
       " ('ieronymos', 0.4736502468585968),\n",
       " ('pengrowth', 0.46680963039398193),\n",
       " ('höss', 0.4636845886707306),\n",
       " ('damaskinos', 0.4617849290370941),\n",
       " ('yadin', 0.4617374837398529),\n",
       " ('hundertwasser', 0.458895742893219),\n",
       " ('ncpa', 0.45773395895957947),\n",
       " ('maccormac', 0.4566110372543335),\n",
       " ('rothfeld', 0.45239466428756714)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.most_similar(negative='banana')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "queen: 0.7699\n"
     ]
    }
   ],
   "source": [
    "result = model.most_similar(positive=['woman', 'king'], negative=['man'])\n",
    "print(\"{}: {:.4f}\".format(*result[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x1 : x2 :: y1 :: returned\n",
    "def analogy(x1, x2, y1):\n",
    "    result = model.most_similar(positive=[y1, x2], negative=[x1])\n",
    "    return result[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Analogy](imgs/word2vec-king-queen-composition.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'queen'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('man', 'king', 'woman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'champagne'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('australia', 'beer', 'france')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'photographing'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('pencil', 'sketching', 'camera')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nixon'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('obama', 'clinton', 'reagan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'longest'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogy('tall', 'tallest', 'long')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cereal\n"
     ]
    }
   ],
   "source": [
    "print(model.doesnt_match(\"breakfast cereal dinner lunch\".split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_pca_scatterplot(model, words=None, sample=0):\n",
    "    if words == None:\n",
    "        if sample > 0:\n",
    "            words = np.random.choice(list(model.vocab.keys()), sample)\n",
    "        else:\n",
    "            words = [ word for word in model.vocab ]\n",
    "        \n",
    "    word_vectors = np.array([model[w] for w in words])\n",
    "\n",
    "    twodim = PCA().fit_transform(word_vectors)[:,:2]\n",
    "    \n",
    "    plt.figure(figsize=(6,6))\n",
    "    plt.scatter(twodim[:,0], twodim[:,1], edgecolors='k', c='r')\n",
    "    for word, (x,y) in zip(words, twodim):\n",
    "        plt.annotate(word, (x, y), xytext=(x+0.05, y+0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "display_pca_scatterplot(model, \n",
    "    ['coffee', 'tea', 'beer', 'wine', 'brandy', 'rum', 'champagne', 'water',\n",
    "        'spaghetti', 'borscht', 'hamburger', 'pizza', 'falafel', 'sushi', 'meatballs',\n",
    "        'dog', 'horse', 'cat', 'monkey', 'parrot', 'koala', 'lizard',\n",
    "        'frog', 'toad', 'monkey', 'ape', 'kangaroo', 'wombat', 'wolf',\n",
    "        'france', 'germany', 'hungary', 'luxembourg', 'australia', 'china', 'iran',\n",
    "        'homework', 'assignment', 'problem', 'exam', 'test', 'class',\n",
    "        'school', 'college', 'university', 'institute'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
